The present invention relates generally to a road recognition system for detecting the road condition ahead of a motor vehicle, and more particularly relates to a system using image processing to inform the driver and optimize braking performance based on the detected road condition.
Many different devices and methods have been employed to determine road condition, and more specifically the surface condition or coefficient of friction between a vehicle tire and the road. For example, various structures have been developed that are formed directly into the tire of a vehicle. Typically, these structures come into contact with the road for detecting the surface coefficient of friction. Unfortunately, such systems require complex structures which can be difficult to apply to existing tires. Further, application directly to the tire makes replacement or repair of the tire very costly or very complicated.
Additional drawbacks to existing road condition detection systems include a limited sensing capability. While an estimated surface coefficient of friction may be detected, other data regarding the road condition, such as the type of surface, is not detected. Accordingly, there exists a need to provide a road recognition system with can easily be applied to both new and existing vehicles without altering the tire structure or requiring complicated structural enhancements, while also providing a robust detection system for supplying information regarding the road condition including the surface condition.
The present invention provides an apparatus and method for detecting the road surface condition for use in a motor vehicle. The system and method detect road data through a temperature sensor, an ultrasonic sensor, and a camera. These sensors provide temperature data, roughness data, and image data, respectively. Subsequently, the road data is filtered for easier processing. A comparison of the filtered road data to reference data is made, and a confidence level of that comparison is generated. Based on the comparison of filter road data to reference data, the road service condition is determined. Finally, a reliability number of the road surface condition determination is made based on the confidence level.
Preferably, the driver is informed of the road surface condition when the reliability number is above a predetermined value. Similarly, stability control systems are optimized in accordance with the detected road surface condition when the reliability number is above a predetermined value. Filtering the road data can include compressing the image data. In turn, compression of the image data may be accomplished in many different ways, including utilizing edge detection, line detection, softening techniques and recognition of color and brightness. A threshold frequency can be employed for filtering ultrasonic data. An average of the ultrasonic data over a set period of time, or a Fourier transform, may be utilized.
The comparison of filtered road data to reference data may include determining an environmental classification of the road surface condition, such as dry, ice, snow, or water. A surface classification of the road surface condition may also be determined, such as concrete, asphalt, dirt, grass, sand, or gravel. The confidence level is determined by the correlation between the road data and reference data. Preferably, the reliability number is based not only on the confidence level, but also on the consistency of the road and filter data for a given period of time, and the amount of noise in the road data prior to filtering. The driver is preferably informed through images or text on a traditional display panel. Vehicle stability systems include such systems as interlock braking systems, traction control systems, yaw and roll stability systems and the like.